create.autoencoder: Create an autoencoder for dimensionality reduction

In iSubGen: Integrative Subtype Generation

Description

Create an autoencoder for dimensionality reduction using keras and tensorflow packages

Usage

create.autoencoder(data.type, data.matrix, encoder.layers.node.nums = c(15,2),
autoencoder.activation = 'tanh', optimization.loss.function = 'mean_squared_error', 
model.file.output.dir = '.')

Arguments

data.type	data type ID. The ID will be used for naming the output file
data.matrix	matrix with data features as rows and patients as columns
encoder.layers.node.nums	vector with the number of nodes for each layer when the reducing the feature dimensions within the autoencoder. The autoencoder will be made symmetrically so the number of nodes in each layer will be used in reverse, not repeating the last layer to re encode the features in the autoencoder
autoencoder.activation	activation function to use in the autoencoder
optimization.loss.function	loss function used for optimization while fitting the autoencoder
model.file.output.dir	file location for the autoencoder file

Value

autoencoder	the autoencoder created by the keras package
autoencoder.file	the hdf5 file that the model was saved in and can be loaded from

Author(s)

Natalie Fox

Examples

## Not run: 

example.molecular.data.dir <- paste0(path.package('iSubGen'),'/exdata/');

ae.result <- create.autoencoder(
  data.type = 'cna',
  data.matrix = load.molecular.aberration.data(
    paste0(example.molecular.data.dir,'cna_profiles.txt'),
    patients = c(paste0('EP00',1:9), paste0('EP0',10:30))
    ),
  encoder.layers.node.nums = c(15,5,2)
  );

## End(Not run)

iSubGen documentation built on April 22, 2021, 5:11 p.m.